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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Process-structure-property relationships for droplet-on-demand liquid-metal-jetted parts

Liquid metal jetting droplet-on-demand additive manufacturing is an attractive alternative to industry-standard, laser-based metal additive manufacturing techniques that use powder feedstocks and produce large heat affected zones. Liquid metal jetting provides a low feedstock footprint, minimal materials waste, less contamination and inclusions from feedstock, and is compatible with a large range of materials. However, little has been reported on the liquid metal jetting process-structure-property relationships that are crucial to understanding how to consistently print high quality metal parts. We investigate the effects of build plate temperature, infill pattern, and track raster rate on the macro- and microstructure, density, hardness, and tensile strength of metal parts created with our custom droplet-on-demand setup using pure tin feedstock as a surrogate for more relevant structural materials. We found that although the build plate temperature had the largest influence on the structure and properties, the infill pattern is an important factor for optimal structural integrity. It was observed that the track raster rate influenced track-to-track porosity and should be considered in toolpath development. Despite printing in ambient air, we could not identify oxide on the surface or in the bulk of the parts and saw no adverse effects on microstructure or tensile performance. The results and methods of this study will help guide future work in optimizing the liquid metal jetting process to reliably print parts using any liquid metal jetting system for a wide variety of metal feedstocks.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Electronic, magnetic, and structural properties of CoVMnSb: Ab initio study

We present computational results on electronic, magnetic, and structural properties of CoVMnSb, a quaternary Heusler alloy. Our calculations indicate that this material may crystallize in two energetically close structural phases: inverted and regular cubic. The inverted cubic phase is the ground state, with ferrimagnetic alignment, and around 80% spin polarization. Despite having a relatively large bandgap in the minority-spin channel close to the Fermi level, this phase does not undergo a half-metallic transition under pressure. This is explained by the “pinning” of the Fermi level at the minority-spin states at the Γ point. At the same time, the regular cubic phase is half-metallic and retains its perfect spin polarization under a wide range of mechanical strain. Transition to a regular cubic phase may be attained by applying uniform pressure (but not biaxial strain). In practice, this pressure may be realized by an atomic substitution of non-magnetic atoms (Sb) with another non-magnetic atom (Si) of a smaller radius. Our calculations indicate that 25% substitution of Sb with Si results in a half-metallic regular cubic phase being the ground state. In addition, CoVMnSb0.5Si0.5 retains its half-metallic properties under a considerable range of mechanical pressure, as well as exhibits thermodynamic stability, thus making this alloy attractive for potential spintronic applications. We hope that the presented results will stimulate experimental efforts to synthesize this compound.

Physics↗
DOE OSTI2026

Sequence-Structure–Property Relationships in Short-Chain Polyesters: How Primary Structure Governs Macroscopic Performance

While polymer properties are fundamentally linked to their nanostructure, the influence of monomer sequence remains less understood than stereochemical factors like tacticity. This study examines how sequence distribution affects the thermal behavior and morphology of homo- and copolyesters, specifically comparing polymers derived from constitutionally identical monomers but with varying degrees of sequence regularity depending on monomer structure or polymerization selectivity. Our findings show that increasing sequence defects progressively diminish thermal stability, crystallinity, melting temperatures, and morphological order. As new materials become more compositionally complex, this work underscores the importance of sequence control in the design of advanced polymers for emerging applications.

Bocharova, Vera [Oak Ridge National Laboratory (OR↗
DOE OSTI2024

Design of intrinsically disordered protein variants with diverse structural properties

Intrinsically disordered proteins (IDPs) perform a broad range of functions in biology, suggesting that the ability to design IDPs could help expand the repertoire of proteins with novel functions. Computational design of IDPs with specific conformational properties has, however, been difficult because of their substantial dynamics and structural complexity. We describe a general algorithm for designing IDPs with specific structural properties. We demonstrate the power of the algorithm by generating variants of naturally occurring IDPs that differ in compaction, long-range contacts, and propensity to phase separate. We experimentally tested and validated our designs and analyzed the sequence features that determine conformations. We show how our results are captured by a machine learning model, enabling us to speed up the algorithm. Our work expands the toolbox for computational protein design and will facilitate the design of proteins whose functions exploit the many properties afforded by protein disorder.

Science & Technology - Other Topics↗
DOE OSTI2024

Tutorial on In Situ and Operando (Scanning) Transmission Electron Microscopy for Analysis of Nanoscale Structure–Property Relationships

In situ and operando (scanning) transmission electron microscopy [(S)TEM] is a powerful characterization technique that uses imaging, diffraction, and spectroscopy to gain nano-to-atomic scale insights into the structure–property relationships in materials. This technique is both customizable and complex because many factors impact the ability to collect structural, compositional, and bonding information from a sample during environmental exposure or under application of an external stimulus. In the past two decades, in situ and operando (S)TEM methods have diversified and grown to encompass additional capabilities, higher degrees of precision, dynamic tracking abilities, enhanced reproducibility, and improved analytical tools. Much of this growth has been shared through the community and within commercialized products that enable rapid adoption and training in this approach. This tutorial aims to serve as a guide for students, collaborators, and nonspecialists to learn the important factors that impact the success of in situ and operando (S)TEM experiments and assess the value of the results obtained. As this is not a step-by-step guide, readers are encouraged to seek out the many comprehensive resources available for gaining a deeper understanding of in situ and operando (S)TEM methods, property measurements, data acquisition, reproducibility, and data analytics.

(S)TEM↗
DOE OSTI2022

The influence of molecular design on structure–property relationships of a supramolecular polymer prodrug

Supramolecular self-assemblies of hydrophilic macromolecules functionalized with hydrophobic, structure-directing components have long been used for drug delivery. In these systems, loading of poorly soluble compounds is typically achieved through physical encapsulation during or after formation of the supramolecular assembly, resulting in low encapsulation efficiencies and limited control over release kinetics, which are predominately governed by diffusion and carrier degradation. To overcome these limitations, amphiphilic prodrugs that leverage a hydrophobic drug as both the therapeutic and structure-directing component can be used to create supramolecular materials with higher loading and controlled-release kinetics using biodegradable or enzymatically cleavable linkers. Here, we report the design, synthesis, and characterization of a library of supramolecular polymer prodrugs based on poly(ethylene glycol) (PEG) and the proregenerative drug 1,4-dihydrophenonthrolin-4-one-3-carboxylic acid (DPCA). Structure–property relationships were elucidated through experimental characterization of prodrug behavior in both the wet and dry states using scattering techniques and electron microscopy and corroborated by coarse-grained modeling. Molecular architecture and the hydrophobic-to-hydrophilic ratio of PEG–DPCA conjugates strongly influenced their physical state in water, ranging from fully soluble to supramolecular spherical assemblies and nanofibers. Molecular design and supramolecular structure, in turn, were shown to dramatically alter hydrolytic and enzymatic release and cellular transport of DPCA. In addition to potentially expanding therapeutic options for DPCA through control of supramolecular assemblies, the design principles elaborated here may inform the development of other supramolecular prodrugs based on hydrophobic small-molecule compounds.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗
DOE OSTI2022

Sparse Data Machine Learning Integration with Theory, Experiment and Uncertainty Quantification: Process-Structure-Property-Performance of Friction Deformation Processing

Computer vision and deep learning tools that advance the ability to establish processing-structure-property-performance (PSPP) relations are presented. The Bayesian binning method for image segmentation enables quantitative analysis of microstructural features in an automated way, while the analysis of shapes and relative orientation of these features reveals local deformation maps indicative of both, material flow and residual stresses due to materials processing. The deep learning method leads to the previous knowledge agnostic mapping of empirically observed microstructural zones in friction stir welding (FSW) process and synthetic microstructure generation capability that is statistically equivalent to experimentally collected data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Materials structure–property factorization for identification of synergistic phase interactions in complex solar fuels photoanodes

Abstract Properties can be tailored by tuning composition in high-order composition spaces. For spaces with complex phase behavior, modeling the properties as a function of composition and phase distribution remains a formidable challenge. We present materials structure–property factorization (MSPF) as an approach to automate modeling of such data and identify synergistic phase interactions. MSPF is an interpretable machine learning algorithm that couples phase mapping via Deep Reasoning Networks (DRNets) to matrix factorization-based modeling of the representative properties of each phase in a dataset. MSPF is demonstrated for Bi–Cu–V oxide photoanodes for solar fuel generation, which contains 25 different phase combinations and correspondingly exhibits complex composition-structure-photoactivity relationships. Comparing the measured photoactivity to a learned model for non-interacting phases, synergistic phase interactions are identified to guide further photoactivity optimization and understanding. MSPF identifies synergistic interactions of a BiVO 4 -like phase with both Cu 2 V 2 O 7 -like and CuV 2 O 6 -like phases, creating avenues for understanding complex photoelectrocatalysts.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Structure–property relationships of reduced graphene oxide membranes intercalated with polycyclic aromatics

Graphene oxide (GO) membranes intercalated with various organic moieties have shown excellent potential for a range of water processing applications. However, microstructure–functional property relationships in these structurally disordered membranes are not well understood. We demonstrate a practical methodology for developing such relationships for GO membranes intercalated with molecular species, with polycyclic aromatic toluidine blue O (TBO) as an example functional intercalant. We use solid-state UV–vis absorbance and fluorescence measurements to quantitatively track the arrangements of TBO in a series of TBO-loaded reduced GO (rGO) membranes. This study reveals the evolution of diverse arrangements including TBO monomers, lateral and stacked dimers, and other aggregates, as a function of overall TBO loading. These microstructures are then correlated to changes in overall properties such as interlayer d-spacings, permeate fluxes, and solute rejections. The characterization of these different intercalant microstructures explains non-intuitive flux and rejection trends, which can circumvent flux and solute rejection trade-offs.

02 PETROLEUM↗
DOE OSTI2023

Chemical structure–property relationships in nanocelluloses

Abstract Nanocelluloses, nanoscale cellulosic particles or fibrils, are an appealing class of nanomaterials with enormous potential for sustainable development. They have great promise to target some of the 17 Sustainable Development Goals outlined by the United Nations. Nanocelluloses are derived from the most abundant biopolymer in the world, cellulose, which have multiple hydroxyl groups on their surface, enabling a vast range of chemical modifications. The interplay between the chemical structure and physicochemical properties of nanocelluloses is crucial to engineering the next generation of green, functional nanomaterials. In this review paper, we provide a comprehensive account of the structure–property relationships in nanocelluloses, which may establish the basis for the design of precision bio‐based materials. Chemical modifications of nanocelluloses, including hydrolysis, oxidation, esterification, amidation, and polymer grafting, provide an array of chemical and physical properties that open opportunities in a diverse spectrum of applications. We review how chemical modifications affect the physicochemical properties of nanocelluloses, such as thermal stability, hydrophobicity, and dispersibility in nonpolar media. Understanding nanocellulose chemical structure–property relationships is integral to the development of sustainable material platforms based on the most renewable biopolymer on earth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Transferable predictions of energetic and structural properties for refractory solid solution alloys across chemical compositions

We present a data-efficient approach to train graph neural networks (GNNs) on density functional theory (DFT) data for accurate and transferable predictions of energetic and structural properties of refractory solid solution alloys in the niobium-tantalum-vanadium (Nb-Ta-V) chemical space. We start by training the GNN model only on DFT data that describes refractory binary alloys niobium-tantalum (Nb-Ta), niobium-vanadium (Nb-V), and tantalum-vanadium (Ta-V) to predict formation enthalpy and root mean squared displacement. Once trained, the GNN predictions are tested on DFT data describing refractory ternary alloys Nb-Ta-V. While, unsurprisingly, direct transferability from binary to ternary is not sufficiently accurate, augmenting the training with only 1% of the available ternary data (uniformly distributed across the entire range of chemical compositions) improves significantly the quality of the GNN predictions. For comparison, we assess the transferability in the opposite direction by training GNN models on ternary Nb-Ta-V data and making predictions on binaries Nb-Ta, Nb-V, and Ta-V, which exhibits notably higher predictive errors. The proposed methodology, which favors transferability from lower-component to higher-component alloys, offers an efficient path towards avoiding the curse of dimensionality incurred when collecting DFT data for discovery and design of multi-component disordered alloys.

Density functional theory calculations↗
DOE OSTI2021

Structure–Property Correlation of Hierarchically Porous Carbons for Fluorocarbon Adsorption

Although traditional commercially available porous carbon-fluorocarbon working pairs have shown promising applicability for adsorption cooling, advancements in synthetic carbons may further improve performance. Moreover, insights into structure-property relationships that target higher sorption capacities within these synthesized carbons may guide such materials' future design. We utilized hierarchically porous synthetic carbons (HPCs) with colossal microporous and mesoporous characterized by high surface areas (up to 2689 m 2 /g) and pore volumes (up to 10.31 cm 3 /g) towards fluorocarbon R134a adsorption. This unique pore topology leads to exceptional R134a uptake, ~250 wt.%, outperforming the highest uptake carbon material to date, Maxsorb III (~220 wt.%). Additionally, material characterizations reveal that the outstanding R134a capacity may be attributed to textural properties and oxygen-terminated functional groups more than graphitization of the material. Most importantly, HPCs are efficiently utilized in a two-bed model chiller device, where the performance shows excellent working capacity (105 wt%, ~1.5 times the value of reported carbon materials/R134a). Fluorocarbons adsorption on HPCs also displays fast kinetics (equilibrium time: ~2 min) mainly driven by physical adsorption (Qst: ~27 kJ/mol), characteristic of swiftly reversible behavior adsorption-desorption behaviors. This work provides a fundamental understanding of the applicability of HPCs/R134a working pair for adsorption cooling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.

Ascencio-Medina, Estefania↗
DOE OSTI2026

Structure-property relations of oP32-Ge under pressure

The unit-cell parameters, systematic absences, and atomic parameters of the germanium allotrope oP32-Ge show continuous changes up to 6 GPa as shown by single-crystal diffraction analysis. Peak shape and diffuse scattering features remain about unchanged throughout the experiment. Hence, neither the average nor the defect structure behavior offers a structure-property explanation for pressure-induced superconductivity observed at about 2 GPa [1]. Angle dispersive x-ray diffraction reveals a sluggish transformation of oP32-Ge to the Ge-II polymorph, the transition begins around 8 GPa and is complete at 12 GPa, a behavior similar to that of Ge-I. The reconstructive transition was associated with the breakdown of crystals into a powder that was compressed to 32 GPa. Synchrotron infrared absorption measurements up to 8.5 GPa indicate an increase of the band gap of the oP32-Ge prior to its collapse at 8.5 GPa. We suggest that the onset of superconductivity is not associated with a major structural change or that such a change occurs in specific stress and temperature conditions.

Lavina, Barbara↗
DOE OSTI2026

Structure–Property Relationships of Recycled Lithium-Ion Battery Cathodes: Microstructure Optimization Using Virtual Materials Testing

The increasing demand for sustainable battery technologies requires effective recycling strategies for end-of-life lithium-ion battery cathodes. In this study, virtual materials testing, a well-established framework for modeling conventionally manufactured NMC-based cathodes, is applied to partially recycled cathodes. To this end, virtual cathodes consisting of mixtures of pristine and recycled NMC particles are utilized to systematically analyze structure–property relationships depending on mixing ratios and different spatial arrangement strategies. For this purpose, a stochastic 3D model is developed that is capable of generating virtual cathodes with arbitrary volume fractions of active materials and mixing ratios of pristine and recycled NMC particles. Particularly, the stochastic 3D model can mimic the different size distributions of pristine and recycled particles that are observed in image data. Additionally, the model allows the structuring of pristine and recycled NMC either uniformly mixed or layer-wise arranged, mimicking single- and dual-layer cathodes. Subsequently, a systematic computational analysis is conducted to assess the influence of increasing active material ratios of recycled particles, ranging from 0 % to 100 %, while maintaining a constant overall active material volume fraction. The impact of particle mixing on cathode performance is evaluated by examining transport-relevant geometrical descriptors and effective properties, such as geodesic tortuosity, specific surface area, and tortuosity factor.

25 ENERGY STORAGE↗